High resolution burn severity data (derived from satellite imagery) for the South Fork McKenzie River in Oregon, USA, before and after a wildfire event, 2020 and 2021
Cite this dataset as:
Dugdale, S.J.; Field, R.; Johnson, M.; Mariani, M.; Pugh, B.; Schrodt, F.; Thorne, C. (2023). High resolution burn severity data (derived from satellite imagery) for the South Fork McKenzie River in Oregon, USA, before and after a wildfire event, 2020 and 2021. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/8162887a-5481-440f-a7f2-427eee793efd
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Contains modified Copernicus Sentinel data [2020, 2021]
This dataset is available under the terms of the Open Government Licence 
https://doi.org/10.5285/8162887a-5481-440f-a7f2-427eee793efd
The data comprise Sentinel-2 derived burn severity rasters covering restored and unrestored reaches of the South Fork McKenzie river, Oregon USA. The data were collected in order to quantify differences in burn severity in restored and unrestored river reaches following the Holiday Farm wildfire in 2020.
Raw satellite imagery acquired in June 2020 and June 2021 was processed to calculate Normalised Burn Ratio (NBR), giving pre- and post-fire burn severity information. Data consist of 10 m .TIF raster imagery where a digital number gives a measure of burn severity; high NBR values indicate healthy vegetation, whereas lower values indicate burnt areas or bare ground.
The study was conducted by the University of Nottingham, in partnership with the US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council.
Raw satellite imagery acquired in June 2020 and June 2021 was processed to calculate Normalised Burn Ratio (NBR), giving pre- and post-fire burn severity information. Data consist of 10 m .TIF raster imagery where a digital number gives a measure of burn severity; high NBR values indicate healthy vegetation, whereas lower values indicate burnt areas or bare ground.
The study was conducted by the University of Nottingham, in partnership with the US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council.
Publication date: 2023-01-17
View numbers valid from 01 June 2023 Download numbers valid from 20 June 2024 (information prior to this was not collected)
Format
TIFF
Spatial information
Study area
Spatial representation type
Raster
Spatial reference system
WGS 84
Temporal information
Temporal extent
2020-01-01 to 2021-12-30
Provenance & quality
20 m bands of Two Sentinel-2A images of study site (pre/post-fire; June 2020 and 2021) were super-resolved to 10m following Lanaras et al (2018)
These data were subsequently used to generate raster files depicting 10m resolution Normalised Burn Ratio (NBR) across South Fork McKenzie River, where NBR = (Sentinel Band B08 - B12) / (B08 + B12), following the methodology outlined in Keeley (2009). Subtraction of postfire from prefire NBR rasters facilitates estimation of burn severity by highlighting vegetation burn in relation to pre-burnt state.
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319 https://doi.org/10.1016/j.isprsjprs.2018.09.018
Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, 116-126. https://doi.org/10.1071/WF07049
These data were subsequently used to generate raster files depicting 10m resolution Normalised Burn Ratio (NBR) across South Fork McKenzie River, where NBR = (Sentinel Band B08 - B12) / (B08 + B12), following the methodology outlined in Keeley (2009). Subtraction of postfire from prefire NBR rasters facilitates estimation of burn severity by highlighting vegetation burn in relation to pre-burnt state.
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319 https://doi.org/10.1016/j.isprsjprs.2018.09.018
Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, 116-126. https://doi.org/10.1071/WF07049
Licensing and constraints
This dataset is available under the terms of the Open Government Licence
Cite this dataset as:
Dugdale, S.J.; Field, R.; Johnson, M.; Mariani, M.; Pugh, B.; Schrodt, F.; Thorne, C. (2023). High resolution burn severity data (derived from satellite imagery) for the South Fork McKenzie River in Oregon, USA, before and after a wildfire event, 2020 and 2021. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/8162887a-5481-440f-a7f2-427eee793efd
Contains modified Copernicus Sentinel data [2020, 2021]
Related
This dataset is included in the following collections
Supplemental information
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319
Keeley, J.E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, 116-126.
Correspondence/contact details
Other contacts
Rights holder
University of Nottingham
Custodian
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Publisher
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk